matrixStats.benchmarks

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colAlls() and rowAlls() benchmarks on subsetted computation

This report benchmark the performance of colAlls() and rowAlls() on subsetted computation.

Data

> rmatrix <- function(nrow, ncol, mode = c("logical", "double", "integer", "index"), range = c(-100, 
+     +100), na_prob = 0) {
+     mode <- match.arg(mode)
+     n <- nrow * ncol
+     if (mode == "logical") {
+         x <- sample(c(FALSE, TRUE), size = n, replace = TRUE)
+     }     else if (mode == "index") {
+         x <- seq_len(n)
+         mode <- "integer"
+     }     else {
+         x <- runif(n, min = range[1], max = range[2])
+     }
+     storage.mode(x) <- mode
+     if (na_prob > 0) 
+         x[sample(n, size = na_prob * n)] <- NA
+     dim(x) <- c(nrow, ncol)
+     x
+ }
> rmatrices <- function(scale = 10, seed = 1, ...) {
+     set.seed(seed)
+     data <- list()
+     data[[1]] <- rmatrix(nrow = scale * 1, ncol = scale * 1, ...)
+     data[[2]] <- rmatrix(nrow = scale * 10, ncol = scale * 10, ...)
+     data[[3]] <- rmatrix(nrow = scale * 100, ncol = scale * 1, ...)
+     data[[4]] <- t(data[[3]])
+     data[[5]] <- rmatrix(nrow = scale * 10, ncol = scale * 100, ...)
+     data[[6]] <- t(data[[5]])
+     names(data) <- sapply(data, FUN = function(x) paste(dim(x), collapse = "x"))
+     data
+ }
> data <- rmatrices(mode = "logical")

Results

10x10 matrix

> X <- data[["10x10"]]
> rows <- sample.int(nrow(X), size = nrow(X) * 0.7)
> cols <- sample.int(ncol(X), size = ncol(X) * 0.7)
> X_S <- X[rows, cols]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5160636 275.7    8529671 455.6  8529671 455.6
Vcells 9219569  70.4   31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colAlls_X_S = colAlls(X_S), `colAlls(X, rows, cols)` = colAlls(X, rows = rows, 
+     cols = cols), `colAlls(X[rows, cols])` = colAlls(X[rows, cols]), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5152999 275.2    8529671 455.6  8529671 455.6
Vcells 9194177  70.2   31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowAlls_X_S = rowAlls(X_S), `rowAlls(X, cols, rows)` = rowAlls(X, rows = cols, 
+     cols = rows), `rowAlls(X[cols, rows])` = rowAlls(X[cols, rows]), unit = "ms")

Table: Benchmarking of colAlls_X_S(), colAlls(X, rows, cols)() and colAlls(X[rows, cols])() on 10x10 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colAlls_X_S	0.002986	0.0030440	0.0059195	0.0031130	0.0032130	0.278269
2	colAlls(X, rows, cols)	0.003362	0.0034715	0.0036577	0.0035425	0.0036075	0.010617
3	colAlls(X[rows, cols])	0.003821	0.0040975	0.0042836	0.0042015	0.0043075	0.008599

	expr	min	lq	mean	median	uq	max
1	colAlls_X_S	1.000000	1.000000	1.0000000	1.000000	1.000000	1.0000000
2	colAlls(X, rows, cols)	1.125921	1.140440	0.6179130	1.137970	1.122782	0.0381537
3	colAlls(X[rows, cols])	1.279638	1.346091	0.7236434	1.349663	1.340647	0.0309018

Table: Benchmarking of rowAlls_X_S(), rowAlls(X, cols, rows)() and rowAlls(X[cols, rows])() on 10x10 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	rowAlls_X_S	0.002893	0.0029690	0.0031030	0.0030435	0.003138	0.005617
2	rowAlls(X, cols, rows)	0.003292	0.0034080	0.0063518	0.0034860	0.003609	0.284589
3	rowAlls(X[cols, rows])	0.003771	0.0040265	0.0042005	0.0041450	0.004269	0.006579

	expr	min	lq	mean	median	uq	max
1	rowAlls_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowAlls(X, cols, rows)	1.137919	1.147861	2.046961	1.145392	1.150096	50.665658
3	rowAlls(X[cols, rows])	1.303491	1.356181	1.353672	1.361919	1.360421	1.171266

Figure: Benchmarking of colAlls_X_S(), colAlls(X, rows, cols)() and colAlls(X[rows, cols])() on 10x10 data as well as rowAlls_X_S(), rowAlls(X, cols, rows)() and rowAlls(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAlls_X_S() and rowAlls_X_S() on 10x10 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	rowAlls_X_S	2.893	2.969	3.10302	3.0435	3.138	5.617
1	colAlls_X_S	2.986	3.044	5.91949	3.1130	3.213	278.269

	expr	min	lq	mean	median	uq	max
2	rowAlls_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000
1	colAlls_X_S	1.032147	1.025261	1.907655	1.022836	1.023901	49.5405

Figure: Benchmarking of colAlls_X_S() and rowAlls_X_S() on 10x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

100x100 matrix

> X <- data[["100x100"]]
> rows <- sample.int(nrow(X), size = nrow(X) * 0.7)
> cols <- sample.int(ncol(X), size = ncol(X) * 0.7)
> X_S <- X[rows, cols]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5152729 275.2    8529671 455.6  8529671 455.6
Vcells 9159001  69.9   31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colAlls_X_S = colAlls(X_S), `colAlls(X, rows, cols)` = colAlls(X, rows = rows, 
+     cols = cols), `colAlls(X[rows, cols])` = colAlls(X[rows, cols]), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5152705 275.2    8529671 455.6  8529671 455.6
Vcells 9164054  70.0   31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowAlls_X_S = rowAlls(X_S), `rowAlls(X, cols, rows)` = rowAlls(X, rows = cols, 
+     cols = rows), `rowAlls(X[cols, rows])` = rowAlls(X[cols, rows]), unit = "ms")

Table: Benchmarking of colAlls_X_S(), colAlls(X, rows, cols)() and colAlls(X[rows, cols])() on 100x100 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colAlls_X_S	0.003892	0.0042700	0.0045061	0.0044345	0.0046165	0.010616
2	colAlls(X, rows, cols)	0.005127	0.0055205	0.0058478	0.0057885	0.0059955	0.010613
3	colAlls(X[rows, cols])	0.020120	0.0204070	0.0211569	0.0206730	0.0209035	0.050245

	expr	min	lq	mean	median	uq	max
1	colAlls_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	colAlls(X, rows, cols)	1.317318	1.292857	1.297747	1.305333	1.298711	0.9997174
3	colAlls(X[rows, cols])	5.169579	4.779157	4.695123	4.661856	4.527997	4.7329503

Table: Benchmarking of rowAlls_X_S(), rowAlls(X, cols, rows)() and rowAlls(X[cols, rows])() on 100x100 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	rowAlls_X_S	0.005586	0.0060035	0.0064399	0.0062340	0.0064625	0.020485
2	rowAlls(X, cols, rows)	0.006963	0.0075525	0.0081366	0.0078550	0.0080790	0.029651
3	rowAlls(X[cols, rows])	0.021308	0.0217170	0.0225564	0.0225345	0.0227575	0.031666

	expr	min	lq	mean	median	uq	max
1	rowAlls_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowAlls(X, cols, rows)	1.246509	1.258016	1.263477	1.260026	1.250135	1.447449
3	rowAlls(X[cols, rows])	3.814536	3.617390	3.502615	3.614774	3.521470	1.545814

Figure: Benchmarking of colAlls_X_S(), colAlls(X, rows, cols)() and colAlls(X[rows, cols])() on 100x100 data as well as rowAlls_X_S(), rowAlls(X, cols, rows)() and rowAlls(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAlls_X_S() and rowAlls_X_S() on 100x100 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colAlls_X_S	3.892	4.2700	4.50614	4.4345	4.6165	10.616
2	rowAlls_X_S	5.586	6.0035	6.43988	6.2340	6.4625	20.485

	expr	min	lq	mean	median	uq	max
1	colAlls_X_S	1.000000	1.000000	1.000000	1.000000	1.00000	1.000000
2	rowAlls_X_S	1.435252	1.405972	1.429134	1.405795	1.39987	1.929634

Figure: Benchmarking of colAlls_X_S() and rowAlls_X_S() on 100x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

1000x10 matrix

> X <- data[["1000x10"]]
> rows <- sample.int(nrow(X), size = nrow(X) * 0.7)
> cols <- sample.int(ncol(X), size = ncol(X) * 0.7)
> X_S <- X[rows, cols]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5153714 275.3    8529671 455.6  8529671 455.6
Vcells 9163506  70.0   31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colAlls_X_S = colAlls(X_S), `colAlls(X, rows, cols)` = colAlls(X, rows = rows, 
+     cols = cols), `colAlls(X[rows, cols])` = colAlls(X[rows, cols]), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5153690 275.3    8529671 455.6  8529671 455.6
Vcells 9168559  70.0   31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowAlls_X_S = rowAlls(X_S), `rowAlls(X, cols, rows)` = rowAlls(X, rows = cols, 
+     cols = rows), `rowAlls(X[cols, rows])` = rowAlls(X[cols, rows]), unit = "ms")

Table: Benchmarking of colAlls_X_S(), colAlls(X, rows, cols)() and colAlls(X[rows, cols])() on 1000x10 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colAlls_X_S	0.002936	0.0030405	0.0031403	0.0030970	0.0031845	0.004853
2	colAlls(X, rows, cols)	0.005964	0.0063925	0.0067599	0.0066375	0.0068835	0.011604
3	colAlls(X[rows, cols])	0.019520	0.0198600	0.0203276	0.0200020	0.0201410	0.044120

	expr	min	lq	mean	median	uq	max
1	colAlls_X_S	1.000000	1.00000	1.000000	1.000000	1.000000	1.000000
2	colAlls(X, rows, cols)	2.031335	2.10245	2.152658	2.143203	2.161564	2.391098
3	colAlls(X[rows, cols])	6.648501	6.53182	6.473197	6.458508	6.324698	9.091284

Table: Benchmarking of rowAlls_X_S(), rowAlls(X, cols, rows)() and rowAlls(X[cols, rows])() on 1000x10 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	rowAlls_X_S	0.006817	0.007121	0.0075415	0.0072500	0.0078400	0.013378
2	rowAlls(X, cols, rows)	0.010039	0.010575	0.0109542	0.0108300	0.0111535	0.015914
3	rowAlls(X[cols, rows])	0.024129	0.025382	0.0260856	0.0257075	0.0258975	0.052710

	expr	min	lq	mean	median	uq	max
1	rowAlls_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowAlls(X, cols, rows)	1.472642	1.485044	1.452521	1.493793	1.422640	1.189565
3	rowAlls(X[cols, rows])	3.539534	3.564387	3.458923	3.545862	3.303253	3.940051

Figure: Benchmarking of colAlls_X_S(), colAlls(X, rows, cols)() and colAlls(X[rows, cols])() on 1000x10 data as well as rowAlls_X_S(), rowAlls(X, cols, rows)() and rowAlls(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAlls_X_S() and rowAlls_X_S() on 1000x10 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colAlls_X_S	2.936	3.0405	3.14028	3.097	3.1845	4.853
2	rowAlls_X_S	6.817	7.1210	7.54153	7.250	7.8400	13.378

	expr	min	lq	mean	median	uq	max
1	colAlls_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowAlls_X_S	2.321867	2.342049	2.401547	2.340975	2.461925	2.756645

Figure: Benchmarking of colAlls_X_S() and rowAlls_X_S() on 1000x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

10x1000 matrix

> X <- data[["10x1000"]]
> rows <- sample.int(nrow(X), size = nrow(X) * 0.7)
> cols <- sample.int(ncol(X), size = ncol(X) * 0.7)
> X_S <- X[rows, cols]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5153951 275.3    8529671 455.6  8529671 455.6
Vcells 9164338  70.0   31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colAlls_X_S = colAlls(X_S), `colAlls(X, rows, cols)` = colAlls(X, rows = rows, 
+     cols = cols), `colAlls(X[rows, cols])` = colAlls(X[rows, cols]), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5153927 275.3    8529671 455.6  8529671 455.6
Vcells 9169391  70.0   31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowAlls_X_S = rowAlls(X_S), `rowAlls(X, cols, rows)` = rowAlls(X, rows = cols, 
+     cols = rows), `rowAlls(X[cols, rows])` = rowAlls(X[cols, rows]), unit = "ms")

Table: Benchmarking of colAlls_X_S(), colAlls(X, rows, cols)() and colAlls(X[rows, cols])() on 10x1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colAlls_X_S	0.014275	0.014966	0.0157981	0.0155765	0.0161020	0.038946
2	colAlls(X, rows, cols)	0.015440	0.018539	0.0193727	0.0193605	0.0202080	0.034012
3	colAlls(X[rows, cols])	0.032465	0.033170	0.0340796	0.0338510	0.0346055	0.040146

	expr	min	lq	mean	median	uq	max
1	colAlls_X_S	1.000000	1.000000	1.000000	1.00000	1.000000	1.0000000
2	colAlls(X, rows, cols)	1.081611	1.238741	1.226266	1.24293	1.254999	0.8733118
3	colAlls(X[rows, cols])	2.274256	2.216357	2.157193	2.17321	2.149143	1.0308119

Table: Benchmarking of rowAlls_X_S(), rowAlls(X, cols, rows)() and rowAlls(X[cols, rows])() on 10x1000 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	rowAlls_X_S	0.011972	0.0141355	0.0159544	0.0156340	0.0173540	0.032300
2	rowAlls(X, cols, rows)	0.015308	0.0191515	0.0215045	0.0209420	0.0227685	0.058972
3	rowAlls(X[cols, rows])	0.029147	0.0310165	0.0330767	0.0325825	0.0344510	0.048933

	expr	min	lq	mean	median	uq	max
1	rowAlls_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowAlls(X, cols, rows)	1.278650	1.354851	1.347874	1.339516	1.312003	1.825759
3	rowAlls(X[cols, rows])	2.434597	2.194227	2.073209	2.084080	1.985191	1.514954

Figure: Benchmarking of colAlls_X_S(), colAlls(X, rows, cols)() and colAlls(X[rows, cols])() on 10x1000 data as well as rowAlls_X_S(), rowAlls(X, cols, rows)() and rowAlls(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAlls_X_S() and rowAlls_X_S() on 10x1000 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colAlls_X_S	14.275	14.9660	15.79814	15.5765	16.102	38.946
2	rowAlls_X_S	11.972	14.1355	15.95437	15.6340	17.354	32.300

	expr	min	lq	mean	median	uq	max
1	colAlls_X_S	1.000000	1.0000000	1.000000	1.000000	1.000000	1.0000000
2	rowAlls_X_S	0.838669	0.9445076	1.009889	1.003691	1.077754	0.8293535

Figure: Benchmarking of colAlls_X_S() and rowAlls_X_S() on 10x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

100x1000 matrix

> X <- data[["100x1000"]]
> rows <- sample.int(nrow(X), size = nrow(X) * 0.7)
> cols <- sample.int(ncol(X), size = ncol(X) * 0.7)
> X_S <- X[rows, cols]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5154173 275.3    8529671 455.6  8529671 455.6
Vcells 9187005  70.1   31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colAlls_X_S = colAlls(X_S), `colAlls(X, rows, cols)` = colAlls(X, rows = rows, 
+     cols = cols), `colAlls(X[rows, cols])` = colAlls(X[rows, cols]), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5154149 275.3    8529671 455.6  8529671 455.6
Vcells 9237058  70.5   31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowAlls_X_S = rowAlls(X_S), `rowAlls(X, cols, rows)` = rowAlls(X, rows = cols, 
+     cols = rows), `rowAlls(X[cols, rows])` = rowAlls(X[cols, rows]), unit = "ms")

Table: Benchmarking of colAlls_X_S(), colAlls(X, rows, cols)() and colAlls(X[rows, cols])() on 100x1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colAlls_X_S	0.013862	0.0164300	0.0179002	0.0174005	0.0185255	0.054121
2	colAlls(X, rows, cols)	0.017819	0.0215875	0.0232370	0.0227030	0.0244085	0.058309
3	colAlls(X[rows, cols])	0.137943	0.1502875	0.1604234	0.1617350	0.1678650	0.206960

	expr	min	lq	mean	median	uq	max
1	colAlls_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	colAlls(X, rows, cols)	1.285457	1.313908	1.298145	1.304733	1.317562	1.077382
3	colAlls(X[rows, cols])	9.951161	9.147139	8.962128	9.294848	9.061294	3.824024

Table: Benchmarking of rowAlls_X_S(), rowAlls(X, cols, rows)() and rowAlls(X[cols, rows])() on 100x1000 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	rowAlls_X_S	0.033167	0.0356275	0.0384009	0.0377135	0.0398360	0.071937
2	rowAlls(X, cols, rows)	0.039492	0.0429860	0.0465241	0.0453340	0.0474345	0.086056
3	rowAlls(X[cols, rows])	0.178062	0.1850315	0.1889066	0.1878170	0.1900360	0.239919

	expr	min	lq	mean	median	uq	max
1	rowAlls_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowAlls(X, cols, rows)	1.190702	1.206540	1.211536	1.202063	1.190745	1.196269
3	rowAlls(X[cols, rows])	5.368650	5.193502	4.919325	4.980100	4.770459	3.335127

Figure: Benchmarking of colAlls_X_S(), colAlls(X, rows, cols)() and colAlls(X[rows, cols])() on 100x1000 data as well as rowAlls_X_S(), rowAlls(X, cols, rows)() and rowAlls(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAlls_X_S() and rowAlls_X_S() on 100x1000 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colAlls_X_S	13.862	16.4300	17.90015	17.4005	18.5255	54.121
2	rowAlls_X_S	33.167	35.6275	38.40092	37.7135	39.8360	71.937

	expr	min	lq	mean	median	uq	max
1	colAlls_X_S	1.000000	1.000000	1.000000	1.00000	1.000000	1.000000
2	rowAlls_X_S	2.392656	2.168442	2.145285	2.16738	2.150333	1.329188

Figure: Benchmarking of colAlls_X_S() and rowAlls_X_S() on 100x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

1000x100 matrix

> X <- data[["1000x100"]]
> rows <- sample.int(nrow(X), size = nrow(X) * 0.7)
> cols <- sample.int(ncol(X), size = ncol(X) * 0.7)
> X_S <- X[rows, cols]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5154389 275.3    8529671 455.6  8529671 455.6
Vcells 9187674  70.1   31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colAlls_X_S = colAlls(X_S), `colAlls(X, rows, cols)` = colAlls(X, rows = rows, 
+     cols = cols), `colAlls(X[rows, cols])` = colAlls(X[rows, cols]), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5154365 275.3    8529671 455.6  8529671 455.6
Vcells 9237727  70.5   31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowAlls_X_S = rowAlls(X_S), `rowAlls(X, cols, rows)` = rowAlls(X, rows = cols, 
+     cols = rows), `rowAlls(X[cols, rows])` = rowAlls(X[cols, rows]), unit = "ms")

Table: Benchmarking of colAlls_X_S(), colAlls(X, rows, cols)() and colAlls(X[rows, cols])() on 1000x100 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colAlls_X_S	0.003264	0.0040010	0.0045915	0.004333	0.0046710	0.024459
2	colAlls(X, rows, cols)	0.006482	0.0073745	0.0081240	0.007878	0.0085775	0.018071
3	colAlls(X[rows, cols])	0.126256	0.1358400	0.1436834	0.141249	0.1529790	0.204710

	expr	min	lq	mean	median	uq	max
1	colAlls_X_S	1.000000	1.000000	1.000000	1.00000	1.000000	1.0000000
2	colAlls(X, rows, cols)	1.985907	1.843164	1.769369	1.81814	1.836331	0.7388282
3	colAlls(X[rows, cols])	38.681373	33.951512	31.293683	32.59843	32.750803	8.3695163

Table: Benchmarking of rowAlls_X_S(), rowAlls(X, cols, rows)() and rowAlls(X[cols, rows])() on 1000x100 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	rowAlls_X_S	0.021818	0.0224805	0.0250644	0.0246440	0.0263895	0.048487
2	rowAlls(X, cols, rows)	0.025328	0.0269640	0.0297502	0.0288350	0.0307975	0.056401
3	rowAlls(X[cols, rows])	0.140504	0.1419800	0.1573729	0.1565415	0.1695720	0.239202

	expr	min	lq	mean	median	uq	max
1	rowAlls_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowAlls(X, cols, rows)	1.160876	1.199439	1.186950	1.170062	1.167036	1.163219
3	rowAlls(X[cols, rows])	6.439820	6.315696	6.278739	6.352114	6.425738	4.933322

Figure: Benchmarking of colAlls_X_S(), colAlls(X, rows, cols)() and colAlls(X[rows, cols])() on 1000x100 data as well as rowAlls_X_S(), rowAlls(X, cols, rows)() and rowAlls(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAlls_X_S() and rowAlls_X_S() on 1000x100 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colAlls_X_S	3.264	4.0010	4.59145	4.333	4.6710	24.459
2	rowAlls_X_S	21.818	22.4805	25.06442	24.644	26.3895	48.487

	expr	min	lq	mean	median	uq	max
1	colAlls_X_S	1.000000	1.00000	1.000000	1.000000	1.000000	1.000000
2	rowAlls_X_S	6.684436	5.61872	5.458933	5.687514	5.649647	1.982379

Figure: Benchmarking of colAlls_X_S() and rowAlls_X_S() on 1000x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

Appendix

Session information

R version 4.1.1 Patched (2021-08-10 r80727)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.5 LTS

Matrix products: default
BLAS:   /home/hb/software/R-devel/R-4-1-branch/lib/R/lib/libRblas.so
LAPACK: /home/hb/software/R-devel/R-4-1-branch/lib/R/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] microbenchmark_1.4-7   matrixStats_0.60.1     ggplot2_3.3.5         
[4] knitr_1.33             R.devices_2.17.0       R.utils_2.10.1        
[7] R.oo_1.24.0            R.methodsS3_1.8.1-9001 history_0.0.1-9000    

loaded via a namespace (and not attached):
 [1] Biobase_2.52.0          httr_1.4.2              splines_4.1.1          
 [4] bit64_4.0.5             network_1.17.1          assertthat_0.2.1       
 [7] highr_0.9               stats4_4.1.1            blob_1.2.2             
[10] GenomeInfoDbData_1.2.6  robustbase_0.93-8       pillar_1.6.2           
[13] RSQLite_2.2.8           lattice_0.20-44         glue_1.4.2             
[16] digest_0.6.27           XVector_0.32.0          colorspace_2.0-2       
[19] Matrix_1.3-4            XML_3.99-0.7            pkgconfig_2.0.3        
[22] zlibbioc_1.38.0         genefilter_1.74.0       purrr_0.3.4            
[25] ergm_4.1.2              xtable_1.8-4            scales_1.1.1           
[28] tibble_3.1.4            annotate_1.70.0         KEGGREST_1.32.0        
[31] farver_2.1.0            generics_0.1.0          IRanges_2.26.0         
[34] ellipsis_0.3.2          cachem_1.0.6            withr_2.4.2            
[37] BiocGenerics_0.38.0     mime_0.11               survival_3.2-13        
[40] magrittr_2.0.1          crayon_1.4.1            statnet.common_4.5.0   
[43] memoise_2.0.0           laeken_0.5.1            fansi_0.5.0            
[46] R.cache_0.15.0          MASS_7.3-54             R.rsp_0.44.0           
[49] progressr_0.8.0         tools_4.1.1             lifecycle_1.0.0        
[52] S4Vectors_0.30.0        trust_0.1-8             munsell_0.5.0          
[55] tabby_0.0.1-9001        AnnotationDbi_1.54.1    Biostrings_2.60.2      
[58] compiler_4.1.1          GenomeInfoDb_1.28.1     rlang_0.4.11           
[61] grid_4.1.1              RCurl_1.98-1.4          cwhmisc_6.6            
[64] rappdirs_0.3.3          startup_0.15.0          labeling_0.4.2         
[67] bitops_1.0-7            base64enc_0.1-3         boot_1.3-28            
[70] gtable_0.3.0            DBI_1.1.1               markdown_1.1           
[73] R6_2.5.1                lpSolveAPI_5.5.2.0-17.7 rle_0.9.2              
[76] dplyr_1.0.7             fastmap_1.1.0           bit_4.0.4              
[79] utf8_1.2.2              parallel_4.1.1          Rcpp_1.0.7             
[82] vctrs_0.3.8             png_0.1-7               DEoptimR_1.0-9         
[85] tidyselect_1.1.1        xfun_0.25               coda_0.19-4            

Total processing time was 11.09 secs.

Reproducibility

To reproduce this report, do:

html <- matrixStats:::benchmark('colRowAlls_subset')

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Copyright Dongcan Jiang. Last updated on 2021-08-25 18:49:36 (+0200 UTC). Powered by RSP.